import pandas as pd
import seaborn as sns
import plotly.express as px
import matplotlib.pyplot as plt
import plotly.io as pio
pio.renderers.default = "plotly_mimetype+notebook"
For this excercise, we have written the following code to load the stock dataset built into plotly express.
stocks = px.data.stocks()
stocks.head()
| date | GOOG | AAPL | AMZN | FB | NFLX | MSFT | |
|---|---|---|---|---|---|---|---|
| 0 | 2018-01-01 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 1 | 2018-01-08 | 1.018172 | 1.011943 | 1.061881 | 0.959968 | 1.053526 | 1.015988 |
| 2 | 2018-01-15 | 1.032008 | 1.019771 | 1.053240 | 0.970243 | 1.049860 | 1.020524 |
| 3 | 2018-01-22 | 1.066783 | 0.980057 | 1.140676 | 1.016858 | 1.307681 | 1.066561 |
| 4 | 2018-01-29 | 1.008773 | 0.917143 | 1.163374 | 1.018357 | 1.273537 | 1.040708 |
Select a stock and create a suitable plot for it. Make sure the plot is readable with relevant information, such as date, values.
fig, goog = plt.subplots(figsize=(12,10))
goog.plot(stocks.date, stocks.GOOG, label = "GOOG")
goog.set_title('Stock price of GOOG')
goog.set_xlabel('Date [yyyy/mm/dd]')
goog.set_ylabel('Stocks value [€]')
goog.set_xticks([0,14,29,44,59,74,89,104])
goog.legend()
plt.show()
You've already plot data from one stock. It is possible to plot multiples of them to support comparison.
To highlight different lines, customise line styles, markers, colors and include a legend to the plot.
fig, goog = plt.subplots(figsize=(12,10))
goog.plot(stocks.date, stocks.GOOG, label = "GOOG")
goog.plot(stocks.date, stocks.AAPL, label = "AAPL")
goog.plot(stocks.date, stocks.AMZN, label = "AMZN")
goog.plot(stocks.date, stocks.FB, label = "FB")
goog.plot(stocks.date, stocks.NFLX, label = "NFLX")
goog.plot(stocks.date, stocks.MSFT, label = "MSFT", linestyle='dashdot', marker='o')
goog.set_title('Stock prices')
goog.set_xlabel('Date [yyyy/mm/dd]')
goog.set_ylabel('Stocks value [€]')
goog.set_xticks([0,14,29,44,59,74,89,104])
goog.legend()
plt.show()
First, load the tips dataset
tips = sns.load_dataset('tips')
tips.head()
| total_bill | tip | sex | smoker | day | time | size | |
|---|---|---|---|---|---|---|---|
| 0 | 16.99 | 1.01 | Female | No | Sun | Dinner | 2 |
| 1 | 10.34 | 1.66 | Male | No | Sun | Dinner | 3 |
| 2 | 21.01 | 3.50 | Male | No | Sun | Dinner | 3 |
| 3 | 23.68 | 3.31 | Male | No | Sun | Dinner | 2 |
| 4 | 24.59 | 3.61 | Female | No | Sun | Dinner | 4 |
Let's explore this dataset. Pose a question and create a plot that support drawing answers for your question.
Some possible questions:
print('What is the difference in tipping between male and female?')
sns.jointplot(x='sex', y='tip', data=tips)
plt.show()
What is the difference in tipping between male and female?
Redo the above exercises (challenges 2 & 3) with plotly express. Create diagrams which you can interact with.
Hints:
fig = px.line(stocks,
x='date',
y=['GOOG','AAPL','AMZN','FB','NFLX','MSFT'],
title='Stocks values'
)
fig.show()
fig = px.histogram(tips,
x='total_bill',
y='tip',
color='sex',
hover_data=tips.columns
)
fig.show()
Recreate the barplot below that shows the population of different continents for the year 2007.
Hints:
#load data
df = px.data.gapminder()
df.head()
| country | continent | year | lifeExp | pop | gdpPercap | iso_alpha | iso_num | |
|---|---|---|---|---|---|---|---|---|
| 0 | Afghanistan | Asia | 1952 | 28.801 | 8425333 | 779.445314 | AFG | 4 |
| 1 | Afghanistan | Asia | 1957 | 30.332 | 9240934 | 820.853030 | AFG | 4 |
| 2 | Afghanistan | Asia | 1962 | 31.997 | 10267083 | 853.100710 | AFG | 4 |
| 3 | Afghanistan | Asia | 1967 | 34.020 | 11537966 | 836.197138 | AFG | 4 |
| 4 | Afghanistan | Asia | 1972 | 36.088 | 13079460 | 739.981106 | AFG | 4 |
df_2007 = df.query('year==2007')
df_2007_new = df_2007.groupby('continent').sum()
fig = px.bar(df_2007_new,
x="pop",
y=df_2007_new.index,
orientation='h',
color=df_2007_new.index)
fig.update_yaxes(categoryorder='total descending')
fig.show()